Guassian pdf is sum of two other RV Integral evaluation -

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    Integral Pdf Sum
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Discussion Overview

The discussion revolves around the evaluation of an integral involving Gaussian random variables, specifically focusing on transforming an expression into a specific exponential form. Participants are attempting to derive the conditional density function of a Gaussian random variable defined as the sum of two other random variables.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant seeks guidance on transforming an integral expression into a specific exponential form using Gaussian properties.
  • Another participant suggests substituting variables, expanding, and completing the square to simplify the expression.
  • Concerns are raised about additional terms that appear during substitution, complicating the transformation into the desired form.
  • Participants discuss the implications of various terms in the exponential expression, including the roles of coefficients and constants.
  • There is mention of confusion regarding the notation and the proper interpretation of terms in the context of the problem.
  • One participant expresses frustration over inconsistencies in the information provided and the clarity of notation used by others.
  • Clarifications are requested regarding the notation used for fractions and the process of completing the square.
  • Participants explore the relationship between the derived terms and the normalization of the distribution.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the correct form of the expression or the steps needed to simplify it. There are multiple competing views on how to approach the problem, and some participants express confusion over the transformations and terms involved.

Contextual Notes

There are unresolved issues regarding the notation and the assumptions made during the derivation process. Some mathematical steps remain unclear, and the discussion reflects varying levels of understanding among participants.

Who May Find This Useful

This discussion may be of interest to those working on problems related to Gaussian distributions, conditional density functions, or integral evaluations in the context of probability and statistics.

  • #31
Hi I have done the integration - but still don't get the result from the book, the terms inside the exp differ in the book to what I have calculated is there some factorization that I am missing - (also in my previous post the k-term dropped a yvalue which has been put back in - apologies)

Your thoughts are appreciated x
 

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  • #32
This is the denominator of the conditional probability expression, right? Well, it's not exactly right but there is a close resemblance. Now you go back and do it over again half a dozen times, looking for the sign errors.
 
  • #33
Thats right - it's almost the denominator - I was just wondering - is there any methods to simply pull out the A^-1B^-1 from the exponential, some factorizing trick. - also I used the subsitutuion in my integral - u=(2k2)^1/2(x-k3). In a previous post you mentions u=(2k2)^-1/2(x-k3), was this merely due to a mistake I made in my notation, would you be-able to clarify that the substitution I have performed is okay? - Thanks
 
  • #34
Yeah, I probably made a mistake.

I think at this point you've basically got it. You just need to go back over your work, fidn the errors. Also, given that you know where you want to end up, check whether, where your answer and their answer seem to differ, they are just different ways of writing the same thing.
 
  • #35
At last - all I needed was [A^-1B^-1]/[A^-1+B^-1] = [A+B]^-1

Thanks for the help - - this is a great forum
 

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